Bibliographic record
Abstract
Goal. To assess rice cultivars for cold tolerance; to select the most valuable in terms of economic characteristics sources for their further use in breeding; to create a trait collection on their basis.Result and Discussion. The article presents the results of studying a rice collection. The collection material is represented by 51 accessions of different eco-geographical origin (European, Eastern, Mid-Asian, Latin American). The accessions belong to two subspecies: japonica and indica. Most of the accessions (22) are from Ukraine and account for 43.1%. Temperature is one of the most important environmental factors affecting the crop establishment and development. In Ukraine, rice is cultivated in the most northern zone of rice growing and, therefore, it is liable to negative effects of cold weather during the seed germination, sprouting, anthesis and grain ripening. Consequently, it is essential for breeders to create cultivars that will be tolerant to low temperatures during the sprouting period, with high field germinability and increased germinative power. Seed germination is particularly affected by temperature. Fifty one rice cultivars were investigated for the seedling development, seed germination rate and seedling growth intensity at 14°C; possible states of embryonic roots were estimated; lengths of these stages were determined. Selection of material with a set of parameters allowed forming a trait collection with increased levels of resistance to abiotic environmental factors. In breeding, it is very important to identify sources of valuable traits and to assess extent of their influence on the plant performance. The research distinguished rice accessions for breeding by levels of cold tolerance, performance and its components. We selected six donors of several valuable traits, the use of which will enhance the breeding efficiency (Kuban 3, Vikont, UkrNDS 8839, Ontario, VNIIR 546, Kaz ER – 6). Thus, the comprehensive assessment of the accessions allowed us not only to replenish the rice gene pool, but also, basing on this research, to choose starting material to involve it in further breeding for creating new cold-tolerant varieties. While creating such cultivars, we revealed morphological traits of rice, which are used as test parameters characterizing the stress tolerance of accessions.Conclusions. The study resulted in formation of the trait collection representing the genetic diversity of cultivars and breeding lines. Each trait and gradation has corresponding standards. Cultivars-standards reflect more specific and stable expression of a trait. The collection was created by 4 basic traits and 3 gradations and contains accessions from 9 countries (Ukraine, Russia, Kazakhstan, Uzbekistan, Kyrgyzstan, USA, Bulgaria, China, and Hungary). The standard accessions selected can serve as sources of economically valuable features to create starting material for breeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".